Overview

Brought to you by YData

Dataset statistics

Number of variables34
Number of observations15494
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 MiB
Average record size in memory272.0 B

Variable types

Text5
Numeric25
DateTime2
Categorical2

Alerts

body_abs_1 is highly overall correlated with body_att_1 and 8 other fieldsHigh correlation
body_att_1 is highly overall correlated with body_abs_1 and 11 other fieldsHigh correlation
body_def_1 is highly overall correlated with body_abs_1 and 7 other fieldsHigh correlation
body_land_1 is highly overall correlated with body_abs_1 and 10 other fieldsHigh correlation
clinch_abs_1 is highly overall correlated with body_abs_1 and 4 other fieldsHigh correlation
clinch_att_1 is highly overall correlated with body_att_1 and 6 other fieldsHigh correlation
clinch_def_1 is highly overall correlated with clinch_abs_1 and 2 other fieldsHigh correlation
clinch_land_1 is highly overall correlated with body_att_1 and 5 other fieldsHigh correlation
ground_abs_1 is highly overall correlated with ground_def_1High correlation
ground_att_1 is highly overall correlated with ground_land_1High correlation
ground_def_1 is highly overall correlated with ground_abs_1High correlation
ground_land_1 is highly overall correlated with ground_att_1High correlation
head_abs_1 is highly overall correlated with body_abs_1 and 7 other fieldsHigh correlation
head_att_1 is highly overall correlated with body_abs_1 and 10 other fieldsHigh correlation
head_def_1 is highly overall correlated with body_abs_1 and 9 other fieldsHigh correlation
head_land_1 is highly overall correlated with body_abs_1 and 8 other fieldsHigh correlation
leg_abs_1 is highly overall correlated with body_abs_1 and 5 other fieldsHigh correlation
leg_att_1 is highly overall correlated with body_att_1 and 4 other fieldsHigh correlation
leg_def_1 is highly overall correlated with leg_abs_1High correlation
leg_land_1 is highly overall correlated with body_att_1 and 4 other fieldsHigh correlation
td_att_1 is highly overall correlated with td_land_1High correlation
td_land_1 is highly overall correlated with td_att_1High correlation
body_abs_1 has 2370 (15.3%) zerosZeros
body_att_1 has 1745 (11.3%) zerosZeros
body_def_1 has 4485 (28.9%) zerosZeros
body_land_1 has 2370 (15.3%) zerosZeros
clinch_abs_1 has 4479 (28.9%) zerosZeros
clinch_att_1 has 3686 (23.8%) zerosZeros
clinch_def_1 has 6447 (41.6%) zerosZeros
clinch_land_1 has 4479 (28.9%) zerosZeros
ground_abs_1 has 6741 (43.5%) zerosZeros
ground_att_1 has 6162 (39.8%) zerosZeros
ground_def_1 has 8619 (55.6%) zerosZeros
ground_land_1 has 6741 (43.5%) zerosZeros
head_abs_1 has 804 (5.2%) zerosZeros
head_att_1 has 205 (1.3%) zerosZeros
head_def_1 has 355 (2.3%) zerosZeros
head_land_1 has 804 (5.2%) zerosZeros
kd_1 has 12648 (81.6%) zerosZeros
leg_abs_1 has 3429 (22.1%) zerosZeros
leg_att_1 has 2961 (19.1%) zerosZeros
leg_def_1 has 7953 (51.3%) zerosZeros
leg_land_1 has 3429 (22.1%) zerosZeros
td_abs_1 has 8396 (54.2%) zerosZeros
td_att_1 has 5363 (34.6%) zerosZeros
td_def_1 has 7278 (47.0%) zerosZeros
td_land_1 has 8396 (54.2%) zerosZeros

Reproduction

Analysis started2024-09-26 01:29:47.767830
Analysis finished2024-09-26 01:30:31.805117
Duration44.04 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct2493
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:32.019491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length22
Mean length13.121854
Min length5

Characters and Unicode

Total characters203310
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique394 ?
Unique (%)2.5%

Sample

1st rowShauna Bannon
2nd rowClaudio Silva
3rd rowOrion Cosce
4th rowMayra Bueno Silva
5th rowYoussef Zalal
ValueCountFrequency (%)
chris 200
 
0.6%
silva 195
 
0.6%
matt 183
 
0.6%
mike 164
 
0.5%
john 148
 
0.5%
alex 143
 
0.5%
anthony 131
 
0.4%
josh 126
 
0.4%
joe 120
 
0.4%
santos 118
 
0.4%
Other values (3214) 30061
95.2%
2024-09-25T19:30:32.370669image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20264
 
10.0%
e 16961
 
8.3%
16095
 
7.9%
n 14312
 
7.0%
i 13756
 
6.8%
o 13224
 
6.5%
r 13003
 
6.4%
l 8893
 
4.4%
s 8074
 
4.0%
t 6236
 
3.1%
Other values (46) 72492
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 20264
 
10.0%
e 16961
 
8.3%
16095
 
7.9%
n 14312
 
7.0%
i 13756
 
6.8%
o 13224
 
6.5%
r 13003
 
6.4%
l 8893
 
4.4%
s 8074
 
4.0%
t 6236
 
3.1%
Other values (46) 72492
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 20264
 
10.0%
e 16961
 
8.3%
16095
 
7.9%
n 14312
 
7.0%
i 13756
 
6.8%
o 13224
 
6.5%
r 13003
 
6.4%
l 8893
 
4.4%
s 8074
 
4.0%
t 6236
 
3.1%
Other values (46) 72492
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 20264
 
10.0%
e 16961
 
8.3%
16095
 
7.9%
n 14312
 
7.0%
i 13756
 
6.8%
o 13224
 
6.5%
r 13003
 
6.4%
l 8893
 
4.4%
s 8074
 
4.0%
t 6236
 
3.1%
Other values (46) 72492
35.7%

body_abs_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5020008
Minimum0
Maximum117
Zeros2370
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:32.465780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q311
95-th percentile25
Maximum117
Range117
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.5914741
Coefficient of variation (CV)1.1452244
Kurtosis8.6701385
Mean7.5020008
Median Absolute Deviation (MAD)4
Skewness2.2490138
Sum116236
Variance73.813428
MonotonicityNot monotonic
2024-09-25T19:30:32.553529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2370
15.3%
1 1676
 
10.8%
2 1367
 
8.8%
3 1134
 
7.3%
4 972
 
6.3%
5 872
 
5.6%
6 795
 
5.1%
7 699
 
4.5%
8 593
 
3.8%
9 559
 
3.6%
Other values (62) 4457
28.8%
ValueCountFrequency (%)
0 2370
15.3%
1 1676
10.8%
2 1367
8.8%
3 1134
7.3%
4 972
6.3%
5 872
 
5.6%
6 795
 
5.1%
7 699
 
4.5%
8 593
 
3.8%
9 559
 
3.6%
ValueCountFrequency (%)
117 1
 
< 0.1%
92 1
 
< 0.1%
89 1
 
< 0.1%
84 1
 
< 0.1%
83 1
 
< 0.1%
73 1
 
< 0.1%
67 2
< 0.1%
66 1
 
< 0.1%
65 1
 
< 0.1%
64 3
< 0.1%

body_att_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.83129
Minimum0
Maximum133
Zeros1745
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:32.640414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q316
95-th percentile34
Maximum133
Range133
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.838145
Coefficient of variation (CV)1.092958
Kurtosis7.029028
Mean10.83129
Median Absolute Deviation (MAD)6
Skewness2.0689464
Sum167820
Variance140.14168
MonotonicityNot monotonic
2024-09-25T19:30:32.806439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1745
 
11.3%
1 1334
 
8.6%
2 1094
 
7.1%
3 909
 
5.9%
4 845
 
5.5%
5 789
 
5.1%
6 677
 
4.4%
7 635
 
4.1%
8 558
 
3.6%
9 554
 
3.6%
Other values (86) 6354
41.0%
ValueCountFrequency (%)
0 1745
11.3%
1 1334
8.6%
2 1094
7.1%
3 909
5.9%
4 845
5.5%
5 789
5.1%
6 677
 
4.4%
7 635
 
4.1%
8 558
 
3.6%
9 554
 
3.6%
ValueCountFrequency (%)
133 1
< 0.1%
127 1
< 0.1%
112 1
< 0.1%
111 1
< 0.1%
105 1
< 0.1%
101 1
< 0.1%
100 1
< 0.1%
98 1
< 0.1%
97 1
< 0.1%
93 1
< 0.1%

body_def_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3292888
Minimum0
Maximum60
Zeros4485
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:32.959980image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile12
Maximum60
Range60
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.3915092
Coefficient of variation (CV)1.3190533
Kurtosis13.024567
Mean3.3292888
Median Absolute Deviation (MAD)2
Skewness2.6987837
Sum51584
Variance19.285353
MonotonicityNot monotonic
2024-09-25T19:30:33.046228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 4485
28.9%
1 2672
17.2%
2 1819
11.7%
3 1416
 
9.1%
4 1044
 
6.7%
5 833
 
5.4%
6 605
 
3.9%
7 555
 
3.6%
8 409
 
2.6%
9 340
 
2.2%
Other values (34) 1316
 
8.5%
ValueCountFrequency (%)
0 4485
28.9%
1 2672
17.2%
2 1819
11.7%
3 1416
 
9.1%
4 1044
 
6.7%
5 833
 
5.4%
6 605
 
3.9%
7 555
 
3.6%
8 409
 
2.6%
9 340
 
2.2%
ValueCountFrequency (%)
60 1
 
< 0.1%
56 1
 
< 0.1%
49 2
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
41 3
< 0.1%
37 2
 
< 0.1%
36 1
 
< 0.1%
35 3
< 0.1%
34 5
< 0.1%

body_land_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5020008
Minimum0
Maximum117
Zeros2370
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:33.138888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q311
95-th percentile25
Maximum117
Range117
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.5914741
Coefficient of variation (CV)1.1452244
Kurtosis8.6701385
Mean7.5020008
Median Absolute Deviation (MAD)4
Skewness2.2490138
Sum116236
Variance73.813428
MonotonicityNot monotonic
2024-09-25T19:30:33.234842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2370
15.3%
1 1676
 
10.8%
2 1367
 
8.8%
3 1134
 
7.3%
4 972
 
6.3%
5 872
 
5.6%
6 795
 
5.1%
7 699
 
4.5%
8 593
 
3.8%
9 559
 
3.6%
Other values (62) 4457
28.8%
ValueCountFrequency (%)
0 2370
15.3%
1 1676
10.8%
2 1367
8.8%
3 1134
7.3%
4 972
6.3%
5 872
 
5.6%
6 795
 
5.1%
7 699
 
4.5%
8 593
 
3.8%
9 559
 
3.6%
ValueCountFrequency (%)
117 1
 
< 0.1%
92 1
 
< 0.1%
89 1
 
< 0.1%
84 1
 
< 0.1%
83 1
 
< 0.1%
73 1
 
< 0.1%
67 2
< 0.1%
66 1
 
< 0.1%
65 1
 
< 0.1%
64 3
< 0.1%

clinch_abs_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0267846
Minimum0
Maximum95
Zeros4479
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:33.329020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile19
Maximum95
Range95
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.3373668
Coefficient of variation (CV)1.4596541
Kurtosis13.677226
Mean5.0267846
Median Absolute Deviation (MAD)2
Skewness2.957192
Sum77885
Variance53.836951
MonotonicityNot monotonic
2024-09-25T19:30:33.424937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4479
28.9%
1 2004
12.9%
2 1516
 
9.8%
3 1178
 
7.6%
4 943
 
6.1%
5 757
 
4.9%
6 625
 
4.0%
7 525
 
3.4%
8 425
 
2.7%
9 354
 
2.3%
Other values (58) 2688
17.3%
ValueCountFrequency (%)
0 4479
28.9%
1 2004
12.9%
2 1516
 
9.8%
3 1178
 
7.6%
4 943
 
6.1%
5 757
 
4.9%
6 625
 
4.0%
7 525
 
3.4%
8 425
 
2.7%
9 354
 
2.3%
ValueCountFrequency (%)
95 1
 
< 0.1%
84 1
 
< 0.1%
78 1
 
< 0.1%
77 1
 
< 0.1%
73 2
< 0.1%
68 1
 
< 0.1%
66 3
< 0.1%
65 1
 
< 0.1%
64 1
 
< 0.1%
61 1
 
< 0.1%

clinch_att_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2321544
Minimum0
Maximum115
Zeros3686
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:33.514537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310
95-th percentile27
Maximum115
Range115
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.9747113
Coefficient of variation (CV)1.3792171
Kurtosis12.580355
Mean7.2321544
Median Absolute Deviation (MAD)4
Skewness2.8200863
Sum112055
Variance99.494865
MonotonicityNot monotonic
2024-09-25T19:30:33.612313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3686
23.8%
1 1577
 
10.2%
2 1251
 
8.1%
3 1121
 
7.2%
4 869
 
5.6%
5 779
 
5.0%
6 647
 
4.2%
7 567
 
3.7%
8 522
 
3.4%
9 447
 
2.9%
Other values (81) 4028
26.0%
ValueCountFrequency (%)
0 3686
23.8%
1 1577
10.2%
2 1251
 
8.1%
3 1121
 
7.2%
4 869
 
5.6%
5 779
 
5.0%
6 647
 
4.2%
7 567
 
3.7%
8 522
 
3.4%
9 447
 
2.9%
ValueCountFrequency (%)
115 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%
105 1
< 0.1%
102 1
< 0.1%
99 1
< 0.1%
94 1
< 0.1%
89 1
< 0.1%
87 1
< 0.1%
86 1
< 0.1%

clinch_def_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2053698
Minimum0
Maximum57
Zeros6447
Zeros (%)41.6%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:33.789361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum57
Range57
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4254329
Coefficient of variation (CV)1.5532238
Kurtosis19.449542
Mean2.2053698
Median Absolute Deviation (MAD)1
Skewness3.309375
Sum34170
Variance11.73359
MonotonicityNot monotonic
2024-09-25T19:30:33.874656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 6447
41.6%
1 2652
17.1%
2 1812
 
11.7%
3 1248
 
8.1%
4 862
 
5.6%
5 621
 
4.0%
6 469
 
3.0%
7 340
 
2.2%
8 224
 
1.4%
9 178
 
1.1%
Other values (30) 641
 
4.1%
ValueCountFrequency (%)
0 6447
41.6%
1 2652
17.1%
2 1812
 
11.7%
3 1248
 
8.1%
4 862
 
5.6%
5 621
 
4.0%
6 469
 
3.0%
7 340
 
2.2%
8 224
 
1.4%
9 178
 
1.1%
ValueCountFrequency (%)
57 1
 
< 0.1%
43 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
38 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
31 5
< 0.1%
30 2
 
< 0.1%

clinch_land_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0267846
Minimum0
Maximum95
Zeros4479
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:33.965643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile19
Maximum95
Range95
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.3373668
Coefficient of variation (CV)1.4596541
Kurtosis13.677226
Mean5.0267846
Median Absolute Deviation (MAD)2
Skewness2.957192
Sum77885
Variance53.836951
MonotonicityNot monotonic
2024-09-25T19:30:34.058315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4479
28.9%
1 2004
12.9%
2 1516
 
9.8%
3 1178
 
7.6%
4 943
 
6.1%
5 757
 
4.9%
6 625
 
4.0%
7 525
 
3.4%
8 425
 
2.7%
9 354
 
2.3%
Other values (58) 2688
17.3%
ValueCountFrequency (%)
0 4479
28.9%
1 2004
12.9%
2 1516
 
9.8%
3 1178
 
7.6%
4 943
 
6.1%
5 757
 
4.9%
6 625
 
4.0%
7 525
 
3.4%
8 425
 
2.7%
9 354
 
2.3%
ValueCountFrequency (%)
95 1
 
< 0.1%
84 1
 
< 0.1%
78 1
 
< 0.1%
77 1
 
< 0.1%
73 2
< 0.1%
68 1
 
< 0.1%
66 3
< 0.1%
65 1
 
< 0.1%
64 1
 
< 0.1%
61 1
 
< 0.1%

ground_abs_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9555957
Minimum0
Maximum136
Zeros6741
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:34.203739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile22
Maximum136
Range136
Interquartile range (IQR)6

Descriptive statistics

Standard deviation9.0220348
Coefficient of variation (CV)1.8205752
Kurtosis18.495915
Mean4.9555957
Median Absolute Deviation (MAD)1
Skewness3.4962426
Sum76782
Variance81.397112
MonotonicityNot monotonic
2024-09-25T19:30:34.321015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6741
43.5%
1 1522
 
9.8%
2 1131
 
7.3%
3 822
 
5.3%
4 662
 
4.3%
5 524
 
3.4%
6 442
 
2.9%
7 380
 
2.5%
8 331
 
2.1%
9 289
 
1.9%
Other values (74) 2650
 
17.1%
ValueCountFrequency (%)
0 6741
43.5%
1 1522
 
9.8%
2 1131
 
7.3%
3 822
 
5.3%
4 662
 
4.3%
5 524
 
3.4%
6 442
 
2.9%
7 380
 
2.5%
8 331
 
2.1%
9 289
 
1.9%
ValueCountFrequency (%)
136 1
< 0.1%
100 1
< 0.1%
96 2
< 0.1%
94 1
< 0.1%
88 2
< 0.1%
86 1
< 0.1%
84 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%
78 2
< 0.1%

ground_att_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct118
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2153737
Minimum0
Maximum163
Zeros6162
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:34.420444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile33
Maximum163
Range163
Interquartile range (IQR)9

Descriptive statistics

Standard deviation12.981461
Coefficient of variation (CV)1.799139
Kurtosis16.468553
Mean7.2153737
Median Absolute Deviation (MAD)2
Skewness3.383902
Sum111795
Variance168.51832
MonotonicityNot monotonic
2024-09-25T19:30:34.527677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6162
39.8%
1 1325
 
8.6%
2 1021
 
6.6%
3 755
 
4.9%
4 611
 
3.9%
5 523
 
3.4%
6 461
 
3.0%
7 400
 
2.6%
8 340
 
2.2%
9 297
 
1.9%
Other values (108) 3599
23.2%
ValueCountFrequency (%)
0 6162
39.8%
1 1325
 
8.6%
2 1021
 
6.6%
3 755
 
4.9%
4 611
 
3.9%
5 523
 
3.4%
6 461
 
3.0%
7 400
 
2.6%
8 340
 
2.2%
9 297
 
1.9%
ValueCountFrequency (%)
163 1
< 0.1%
141 1
< 0.1%
136 1
< 0.1%
135 1
< 0.1%
130 1
< 0.1%
122 1
< 0.1%
121 1
< 0.1%
120 1
< 0.1%
119 2
< 0.1%
116 1
< 0.1%

ground_def_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.259778
Minimum0
Maximum91
Zeros8619
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:34.630124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11
Maximum91
Range91
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.7663364
Coefficient of variation (CV)2.1092056
Kurtosis31.898988
Mean2.259778
Median Absolute Deviation (MAD)0
Skewness4.3842294
Sum35013
Variance22.717963
MonotonicityNot monotonic
2024-09-25T19:30:34.724077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8619
55.6%
1 1884
 
12.2%
2 1205
 
7.8%
3 768
 
5.0%
4 599
 
3.9%
5 411
 
2.7%
6 357
 
2.3%
7 257
 
1.7%
8 198
 
1.3%
9 175
 
1.1%
Other values (43) 1021
 
6.6%
ValueCountFrequency (%)
0 8619
55.6%
1 1884
 
12.2%
2 1205
 
7.8%
3 768
 
5.0%
4 599
 
3.9%
5 411
 
2.7%
6 357
 
2.3%
7 257
 
1.7%
8 198
 
1.3%
9 175
 
1.1%
ValueCountFrequency (%)
91 1
 
< 0.1%
73 1
 
< 0.1%
61 2
< 0.1%
57 1
 
< 0.1%
55 1
 
< 0.1%
50 1
 
< 0.1%
48 1
 
< 0.1%
46 1
 
< 0.1%
44 2
< 0.1%
43 4
< 0.1%

ground_land_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9555957
Minimum0
Maximum136
Zeros6741
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:34.903092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile22
Maximum136
Range136
Interquartile range (IQR)6

Descriptive statistics

Standard deviation9.0220348
Coefficient of variation (CV)1.8205752
Kurtosis18.495915
Mean4.9555957
Median Absolute Deviation (MAD)1
Skewness3.4962426
Sum76782
Variance81.397112
MonotonicityNot monotonic
2024-09-25T19:30:35.023499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6741
43.5%
1 1522
 
9.8%
2 1131
 
7.3%
3 822
 
5.3%
4 662
 
4.3%
5 524
 
3.4%
6 442
 
2.9%
7 380
 
2.5%
8 331
 
2.1%
9 289
 
1.9%
Other values (74) 2650
 
17.1%
ValueCountFrequency (%)
0 6741
43.5%
1 1522
 
9.8%
2 1131
 
7.3%
3 822
 
5.3%
4 662
 
4.3%
5 524
 
3.4%
6 442
 
2.9%
7 380
 
2.5%
8 331
 
2.1%
9 289
 
1.9%
ValueCountFrequency (%)
136 1
< 0.1%
100 1
< 0.1%
96 2
< 0.1%
94 1
< 0.1%
88 2
< 0.1%
86 1
< 0.1%
84 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%
78 2
< 0.1%

head_abs_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct163
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.854524
Minimum0
Maximum274
Zeros804
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:35.123560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median17
Q332
95-th percentile66
Maximum274
Range274
Interquartile range (IQR)25

Descriptive statistics

Standard deviation22.335509
Coefficient of variation (CV)0.9772905
Kurtosis6.7360217
Mean22.854524
Median Absolute Deviation (MAD)12
Skewness1.9714287
Sum354108
Variance498.87498
MonotonicityNot monotonic
2024-09-25T19:30:35.220221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 804
 
5.2%
1 635
 
4.1%
2 534
 
3.4%
3 511
 
3.3%
6 490
 
3.2%
5 456
 
2.9%
4 443
 
2.9%
10 415
 
2.7%
7 412
 
2.7%
8 407
 
2.6%
Other values (153) 10387
67.0%
ValueCountFrequency (%)
0 804
5.2%
1 635
4.1%
2 534
3.4%
3 511
3.3%
4 443
2.9%
5 456
2.9%
6 490
3.2%
7 412
2.7%
8 407
2.6%
9 393
2.5%
ValueCountFrequency (%)
274 1
< 0.1%
244 1
< 0.1%
199 2
< 0.1%
187 1
< 0.1%
180 1
< 0.1%
179 1
< 0.1%
177 1
< 0.1%
173 2
< 0.1%
172 1
< 0.1%
167 1
< 0.1%

head_att_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct342
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.353234
Minimum0
Maximum553
Zeros205
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:35.315187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q120
median48
Q391
95-th percentile176
Maximum553
Range553
Interquartile range (IQR)71

Descriptive statistics

Standard deviation57.244906
Coefficient of variation (CV)0.90358302
Kurtosis3.4911724
Mean63.353234
Median Absolute Deviation (MAD)33
Skewness1.5570373
Sum981595
Variance3276.9793
MonotonicityNot monotonic
2024-09-25T19:30:35.416330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 245
 
1.6%
5 240
 
1.5%
3 232
 
1.5%
4 224
 
1.4%
6 216
 
1.4%
2 208
 
1.3%
8 207
 
1.3%
7 206
 
1.3%
0 205
 
1.3%
14 189
 
1.2%
Other values (332) 13322
86.0%
ValueCountFrequency (%)
0 205
1.3%
1 245
1.6%
2 208
1.3%
3 232
1.5%
4 224
1.4%
5 240
1.5%
6 216
1.4%
7 206
1.3%
8 207
1.3%
9 167
1.1%
ValueCountFrequency (%)
553 1
< 0.1%
454 1
< 0.1%
437 1
< 0.1%
436 1
< 0.1%
430 1
< 0.1%
414 1
< 0.1%
400 1
< 0.1%
397 1
< 0.1%
394 1
< 0.1%
389 1
< 0.1%

head_def_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct246
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.498709
Minimum0
Maximum340
Zeros355
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:35.513098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median29
Q358
95-th percentile118
Maximum340
Range340
Interquartile range (IQR)47

Descriptive statistics

Standard deviation38.830487
Coefficient of variation (CV)0.95880802
Kurtosis3.7225608
Mean40.498709
Median Absolute Deviation (MAD)21
Skewness1.642891
Sum627487
Variance1507.8067
MonotonicityNot monotonic
2024-09-25T19:30:35.617065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 404
 
2.6%
2 376
 
2.4%
3 375
 
2.4%
4 370
 
2.4%
0 355
 
2.3%
5 349
 
2.3%
6 327
 
2.1%
7 301
 
1.9%
8 286
 
1.8%
9 283
 
1.8%
Other values (236) 12068
77.9%
ValueCountFrequency (%)
0 355
2.3%
1 404
2.6%
2 376
2.4%
3 375
2.4%
4 370
2.4%
5 349
2.3%
6 327
2.1%
7 301
1.9%
8 286
1.8%
9 283
1.8%
ValueCountFrequency (%)
340 1
< 0.1%
326 1
< 0.1%
287 2
< 0.1%
284 1
< 0.1%
279 2
< 0.1%
276 1
< 0.1%
272 1
< 0.1%
271 1
< 0.1%
265 1
< 0.1%
261 2
< 0.1%

head_land_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct163
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.854524
Minimum0
Maximum274
Zeros804
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:35.715948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median17
Q332
95-th percentile66
Maximum274
Range274
Interquartile range (IQR)25

Descriptive statistics

Standard deviation22.335509
Coefficient of variation (CV)0.9772905
Kurtosis6.7360217
Mean22.854524
Median Absolute Deviation (MAD)12
Skewness1.9714287
Sum354108
Variance498.87498
MonotonicityNot monotonic
2024-09-25T19:30:35.815025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 804
 
5.2%
1 635
 
4.1%
2 534
 
3.4%
3 511
 
3.3%
6 490
 
3.2%
5 456
 
2.9%
4 443
 
2.9%
10 415
 
2.7%
7 412
 
2.7%
8 407
 
2.6%
Other values (153) 10387
67.0%
ValueCountFrequency (%)
0 804
5.2%
1 635
4.1%
2 534
3.4%
3 511
3.3%
4 443
2.9%
5 456
2.9%
6 490
3.2%
7 412
2.7%
8 407
2.6%
9 393
2.5%
ValueCountFrequency (%)
274 1
< 0.1%
244 1
< 0.1%
199 2
< 0.1%
187 1
< 0.1%
180 1
< 0.1%
179 1
< 0.1%
177 1
< 0.1%
173 2
< 0.1%
172 1
< 0.1%
167 1
< 0.1%

kd_1
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21421195
Minimum0
Maximum5
Zeros12648
Zeros (%)81.6%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:35.898897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49093181
Coefficient of variation (CV)2.291804
Kurtosis8.4396166
Mean0.21421195
Median Absolute Deviation (MAD)0
Skewness2.6084531
Sum3319
Variance0.24101404
MonotonicityNot monotonic
2024-09-25T19:30:36.090184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 12648
81.6%
1 2449
 
15.8%
2 333
 
2.1%
3 54
 
0.3%
4 8
 
0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 12648
81.6%
1 2449
 
15.8%
2 333
 
2.1%
3 54
 
0.3%
4 8
 
0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 8
 
0.1%
3 54
 
0.3%
2 333
 
2.1%
1 2449
 
15.8%
0 12648
81.6%

leg_abs_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9314573
Minimum0
Maximum95
Zeros3429
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:36.174860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile22
Maximum95
Range95
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.8466109
Coefficient of variation (CV)1.3228808
Kurtosis10.397638
Mean5.9314573
Median Absolute Deviation (MAD)3
Skewness2.5903583
Sum91902
Variance61.569303
MonotonicityNot monotonic
2024-09-25T19:30:36.266616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3429
22.1%
1 2061
13.3%
2 1541
9.9%
3 1217
 
7.9%
4 982
 
6.3%
5 760
 
4.9%
6 647
 
4.2%
7 617
 
4.0%
8 503
 
3.2%
9 455
 
2.9%
Other values (60) 3282
21.2%
ValueCountFrequency (%)
0 3429
22.1%
1 2061
13.3%
2 1541
9.9%
3 1217
 
7.9%
4 982
 
6.3%
5 760
 
4.9%
6 647
 
4.2%
7 617
 
4.0%
8 503
 
3.2%
9 455
 
2.9%
ValueCountFrequency (%)
95 1
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%
75 2
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
64 1
< 0.1%

leg_att_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3719504
Minimum0
Maximum102
Zeros2961
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:36.355896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310
95-th percentile27
Maximum102
Range102
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.6230693
Coefficient of variation (CV)1.3053627
Kurtosis10.395619
Mean7.3719504
Median Absolute Deviation (MAD)4
Skewness2.5965495
Sum114221
Variance92.603463
MonotonicityNot monotonic
2024-09-25T19:30:36.453409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2961
19.1%
1 1825
11.8%
2 1409
 
9.1%
3 1158
 
7.5%
4 961
 
6.2%
5 791
 
5.1%
6 672
 
4.3%
7 588
 
3.8%
8 506
 
3.3%
9 471
 
3.0%
Other values (73) 4152
26.8%
ValueCountFrequency (%)
0 2961
19.1%
1 1825
11.8%
2 1409
9.1%
3 1158
 
7.5%
4 961
 
6.2%
5 791
 
5.1%
6 672
 
4.3%
7 588
 
3.8%
8 506
 
3.3%
9 471
 
3.0%
ValueCountFrequency (%)
102 1
 
< 0.1%
101 1
 
< 0.1%
93 1
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
89 1
 
< 0.1%
88 1
 
< 0.1%
87 3
< 0.1%
85 1
 
< 0.1%
83 2
< 0.1%

leg_def_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4404931
Minimum0
Maximum34
Zeros7953
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:36.533771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum34
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5545193
Coefficient of variation (CV)1.7733645
Kurtosis19.264007
Mean1.4404931
Median Absolute Deviation (MAD)0
Skewness3.5398354
Sum22319
Variance6.5255686
MonotonicityNot monotonic
2024-09-25T19:30:36.618033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 7953
51.3%
1 3032
 
19.6%
2 1621
 
10.5%
3 925
 
6.0%
4 579
 
3.7%
5 408
 
2.6%
6 236
 
1.5%
7 185
 
1.2%
8 150
 
1.0%
9 121
 
0.8%
Other values (20) 284
 
1.8%
ValueCountFrequency (%)
0 7953
51.3%
1 3032
 
19.6%
2 1621
 
10.5%
3 925
 
6.0%
4 579
 
3.7%
5 408
 
2.6%
6 236
 
1.5%
7 185
 
1.2%
8 150
 
1.0%
9 121
 
0.8%
ValueCountFrequency (%)
34 1
 
< 0.1%
33 1
 
< 0.1%
27 1
 
< 0.1%
26 3
< 0.1%
25 3
< 0.1%
24 4
< 0.1%
23 4
< 0.1%
22 5
< 0.1%
21 3
< 0.1%
20 4
< 0.1%

leg_land_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9314573
Minimum0
Maximum95
Zeros3429
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:36.709563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile22
Maximum95
Range95
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.8466109
Coefficient of variation (CV)1.3228808
Kurtosis10.397638
Mean5.9314573
Median Absolute Deviation (MAD)3
Skewness2.5903583
Sum91902
Variance61.569303
MonotonicityNot monotonic
2024-09-25T19:30:36.801020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3429
22.1%
1 2061
13.3%
2 1541
9.9%
3 1217
 
7.9%
4 982
 
6.3%
5 760
 
4.9%
6 647
 
4.2%
7 617
 
4.0%
8 503
 
3.2%
9 455
 
2.9%
Other values (60) 3282
21.2%
ValueCountFrequency (%)
0 3429
22.1%
1 2061
13.3%
2 1541
9.9%
3 1217
 
7.9%
4 982
 
6.3%
5 760
 
4.9%
6 647
 
4.2%
7 617
 
4.0%
8 503
 
3.2%
9 455
 
2.9%
ValueCountFrequency (%)
95 1
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%
75 2
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
64 1
< 0.1%

td_abs_1
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0620886
Minimum0
Maximum21
Zeros8396
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:36.879461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6844667
Coefficient of variation (CV)1.5859946
Kurtosis8.5548822
Mean1.0620886
Median Absolute Deviation (MAD)0
Skewness2.4516191
Sum16456
Variance2.8374279
MonotonicityNot monotonic
2024-09-25T19:30:36.952301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 8396
54.2%
1 3223
 
20.8%
2 1596
 
10.3%
3 950
 
6.1%
4 525
 
3.4%
5 352
 
2.3%
6 200
 
1.3%
7 103
 
0.7%
8 56
 
0.4%
9 39
 
0.3%
Other values (7) 54
 
0.3%
ValueCountFrequency (%)
0 8396
54.2%
1 3223
 
20.8%
2 1596
 
10.3%
3 950
 
6.1%
4 525
 
3.4%
5 352
 
2.3%
6 200
 
1.3%
7 103
 
0.7%
8 56
 
0.4%
9 39
 
0.3%
ValueCountFrequency (%)
21 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 9
 
0.1%
11 19
 
0.1%
10 19
 
0.1%
9 39
 
0.3%
8 56
0.4%
7 103
0.7%

td_att_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8083129
Minimum0
Maximum49
Zeros5363
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:37.029995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile11
Maximum49
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7148116
Coefficient of variation (CV)1.3227912
Kurtosis6.1339914
Mean2.8083129
Median Absolute Deviation (MAD)1
Skewness2.056097
Sum43512
Variance13.799825
MonotonicityNot monotonic
2024-09-25T19:30:37.118060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 5363
34.6%
1 2733
17.6%
2 1688
 
10.9%
3 1211
 
7.8%
4 938
 
6.1%
5 738
 
4.8%
6 631
 
4.1%
7 501
 
3.2%
8 364
 
2.3%
9 294
 
1.9%
Other values (22) 1033
 
6.7%
ValueCountFrequency (%)
0 5363
34.6%
1 2733
17.6%
2 1688
 
10.9%
3 1211
 
7.8%
4 938
 
6.1%
5 738
 
4.8%
6 631
 
4.1%
7 501
 
3.2%
8 364
 
2.3%
9 294
 
1.9%
ValueCountFrequency (%)
49 1
 
< 0.1%
33 1
 
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
26 3
 
< 0.1%
25 2
 
< 0.1%
24 4
< 0.1%
23 3
 
< 0.1%
22 8
0.1%

td_def_1
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7462243
Minimum0
Maximum38
Zeros7278
Zeros (%)47.0%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:37.196341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum38
Range38
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7122143
Coefficient of variation (CV)1.5531878
Kurtosis10.523198
Mean1.7462243
Median Absolute Deviation (MAD)1
Skewness2.6177317
Sum27056
Variance7.3561062
MonotonicityNot monotonic
2024-09-25T19:30:37.283364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 7278
47.0%
1 2829
 
18.3%
2 1600
 
10.3%
3 1086
 
7.0%
4 747
 
4.8%
5 554
 
3.6%
6 387
 
2.5%
7 280
 
1.8%
8 209
 
1.3%
9 157
 
1.0%
Other values (16) 367
 
2.4%
ValueCountFrequency (%)
0 7278
47.0%
1 2829
 
18.3%
2 1600
 
10.3%
3 1086
 
7.0%
4 747
 
4.8%
5 554
 
3.6%
6 387
 
2.5%
7 280
 
1.8%
8 209
 
1.3%
9 157
 
1.0%
ValueCountFrequency (%)
38 1
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
22 3
 
< 0.1%
21 3
 
< 0.1%
20 5
 
< 0.1%
19 5
 
< 0.1%
18 5
 
< 0.1%
17 14
0.1%
16 14
0.1%

td_land_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0620886
Minimum0
Maximum21
Zeros8396
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:37.362134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6844667
Coefficient of variation (CV)1.5859946
Kurtosis8.5548822
Mean1.0620886
Median Absolute Deviation (MAD)0
Skewness2.4516191
Sum16456
Variance2.8374279
MonotonicityNot monotonic
2024-09-25T19:30:37.436565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 8396
54.2%
1 3223
 
20.8%
2 1596
 
10.3%
3 950
 
6.1%
4 525
 
3.4%
5 352
 
2.3%
6 200
 
1.3%
7 103
 
0.7%
8 56
 
0.4%
9 39
 
0.3%
Other values (7) 54
 
0.3%
ValueCountFrequency (%)
0 8396
54.2%
1 3223
 
20.8%
2 1596
 
10.3%
3 950
 
6.1%
4 525
 
3.4%
5 352
 
2.3%
6 200
 
1.3%
7 103
 
0.7%
8 56
 
0.4%
9 39
 
0.3%
ValueCountFrequency (%)
21 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 9
 
0.1%
11 19
 
0.1%
10 19
 
0.1%
9 39
 
0.3%
8 56
0.4%
7 103
0.7%

date
Date

Distinct693
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
Minimum1994-03-11 00:00:00
Maximum2024-07-27 00:00:00
2024-09-25T19:30:37.525668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:37.633178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
3
7838 
1
4388 
2
2598 
5
 
582
4
 
88

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15494
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 7838
50.6%
1 4388
28.3%
2 2598
 
16.8%
5 582
 
3.8%
4 88
 
0.6%

Length

2024-09-25T19:30:37.719899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-25T19:30:37.800191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
3 7838
50.6%
1 4388
28.3%
2 2598
 
16.8%
5 582
 
3.8%
4 88
 
0.6%

Most occurring characters

ValueCountFrequency (%)
3 7838
50.6%
1 4388
28.3%
2 2598
 
16.8%
5 582
 
3.8%
4 88
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 7838
50.6%
1 4388
28.3%
2 2598
 
16.8%
5 582
 
3.8%
4 88
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 7838
50.6%
1 4388
28.3%
2 2598
 
16.8%
5 582
 
3.8%
4 88
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 7838
50.6%
1 4388
28.3%
2 2598
 
16.8%
5 582
 
3.8%
4 88
 
0.6%
Distinct336
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
Minimum2024-09-25 00:05:00
Maximum2024-09-25 18:00:00
2024-09-25T19:30:37.893647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:37.991175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

decision_method
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
Decision - Unanimous
5480 
KO/TKO
4892 
Submission
3042 
Decision - Split
1490 
Decision - Majority
 
186
Other values (5)
 
404

Length

Max length23
Median length20
Mean length13.116561
Min length2

Characters and Unicode

Total characters203228
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDecision - Split
2nd rowDecision - Unanimous
3rd rowDecision - Unanimous
4th rowSubmission
5th rowDecision - Majority

Common Values

ValueCountFrequency (%)
Decision - Unanimous 5480
35.4%
KO/TKO 4892
31.6%
Submission 3042
19.6%
Decision - Split 1490
 
9.6%
Decision - Majority 186
 
1.2%
TKO - Doctor's Stoppage 182
 
1.2%
Overturned 114
 
0.7%
Could Not Continue 58
 
0.4%
DQ 46
 
0.3%
Other 4
 
< 0.1%

Length

2024-09-25T19:30:38.085048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-25T19:30:38.164097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
7338
24.1%
decision 7156
23.5%
unanimous 5480
18.0%
ko/tko 4892
16.1%
submission 3042
10.0%
split 1490
 
4.9%
majority 186
 
0.6%
tko 182
 
0.6%
doctor's 182
 
0.6%
stoppage 182
 
0.6%
Other values (6) 338
 
1.1%

Most occurring characters

ValueCountFrequency (%)
i 27610
13.6%
n 21388
 
10.5%
s 18902
 
9.3%
o 16584
 
8.2%
14974
 
7.4%
O 10084
 
5.0%
K 9966
 
4.9%
u 8752
 
4.3%
m 8522
 
4.2%
e 7628
 
3.8%
Other values (24) 58818
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 27610
13.6%
n 21388
 
10.5%
s 18902
 
9.3%
o 16584
 
8.2%
14974
 
7.4%
O 10084
 
5.0%
K 9966
 
4.9%
u 8752
 
4.3%
m 8522
 
4.2%
e 7628
 
3.8%
Other values (24) 58818
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 27610
13.6%
n 21388
 
10.5%
s 18902
 
9.3%
o 16584
 
8.2%
14974
 
7.4%
O 10084
 
5.0%
K 9966
 
4.9%
u 8752
 
4.3%
m 8522
 
4.2%
e 7628
 
3.8%
Other values (24) 58818
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 27610
13.6%
n 21388
 
10.5%
s 18902
 
9.3%
o 16584
 
8.2%
14974
 
7.4%
O 10084
 
5.0%
K 9966
 
4.9%
u 8752
 
4.3%
m 8522
 
4.2%
e 7628
 
3.8%
Other values (24) 58818
28.9%
Distinct169
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:38.363224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length43
Median length40
Mean length24.692526
Min length12

Characters and Unicode

Total characters382586
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManchester, England, United Kingdom
2nd rowLondon, England, United Kingdom
3rd rowDallas, Texas, USA
4th rowLas Vegas, Nevada, USA
5th rowSan Diego, California, USA
ValueCountFrequency (%)
usa 10826
18.4%
las 5188
 
8.8%
nevada 5188
 
8.8%
vegas 5188
 
8.8%
new 1400
 
2.4%
united 1078
 
1.8%
brazil 940
 
1.6%
abu 820
 
1.4%
dhabi 820
 
1.4%
canada 776
 
1.3%
Other values (281) 26468
45.1%
2024-09-25T19:30:38.739958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 45486
 
11.9%
43198
 
11.3%
, 29912
 
7.8%
e 26328
 
6.9%
s 18870
 
4.9%
i 17792
 
4.7%
n 15662
 
4.1%
o 15362
 
4.0%
A 14070
 
3.7%
S 13316
 
3.5%
Other values (45) 142590
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 382586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 45486
 
11.9%
43198
 
11.3%
, 29912
 
7.8%
e 26328
 
6.9%
s 18870
 
4.9%
i 17792
 
4.7%
n 15662
 
4.1%
o 15362
 
4.0%
A 14070
 
3.7%
S 13316
 
3.5%
Other values (45) 142590
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 382586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 45486
 
11.9%
43198
 
11.3%
, 29912
 
7.8%
e 26328
 
6.9%
s 18870
 
4.9%
i 17792
 
4.7%
n 15662
 
4.1%
o 15362
 
4.0%
A 14070
 
3.7%
S 13316
 
3.5%
Other values (45) 142590
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 382586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 45486
 
11.9%
43198
 
11.3%
, 29912
 
7.8%
e 26328
 
6.9%
s 18870
 
4.9%
i 17792
 
4.7%
n 15662
 
4.1%
o 15362
 
4.0%
A 14070
 
3.7%
S 13316
 
3.5%
Other values (45) 142590
37.3%
Distinct113
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:38.846417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length68
Median length67
Mean length18.653027
Min length14

Characters and Unicode

Total characters289010
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWomen's Strawweight Bout
2nd rowWelterweight Bout
3rd rowWelterweight Bout
4th rowWomen's Bantamweight Bout
5th rowBantamweight Bout
ValueCountFrequency (%)
bout 15494
42.4%
heavyweight 2796
 
7.7%
lightweight 2654
 
7.3%
welterweight 2582
 
7.1%
middleweight 2044
 
5.6%
bantamweight 1764
 
4.8%
featherweight 1560
 
4.3%
women's 1550
 
4.2%
light 1382
 
3.8%
flyweight 1144
 
3.1%
Other values (53) 3530
 
9.7%
2024-09-25T19:30:39.059437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 43100
14.9%
e 31632
10.9%
i 22770
 
7.9%
h 21332
 
7.4%
21006
 
7.3%
g 19616
 
6.8%
B 17272
 
6.0%
o 17228
 
6.0%
w 15776
 
5.5%
u 15668
 
5.4%
Other values (41) 63610
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 289010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 43100
14.9%
e 31632
10.9%
i 22770
 
7.9%
h 21332
 
7.4%
21006
 
7.3%
g 19616
 
6.8%
B 17272
 
6.0%
o 17228
 
6.0%
w 15776
 
5.5%
u 15668
 
5.4%
Other values (41) 63610
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 289010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 43100
14.9%
e 31632
10.9%
i 22770
 
7.9%
h 21332
 
7.4%
21006
 
7.3%
g 19616
 
6.8%
B 17272
 
6.0%
o 17228
 
6.0%
w 15776
 
5.5%
u 15668
 
5.4%
Other values (41) 63610
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 289010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 43100
14.9%
e 31632
10.9%
i 22770
 
7.9%
h 21332
 
7.4%
21006
 
7.3%
g 19616
 
6.8%
B 17272
 
6.0%
o 17228
 
6.0%
w 15776
 
5.5%
u 15668
 
5.4%
Other values (41) 63610
22.0%

winner
Text

Distinct1734
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:39.340707image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length22
Mean length12.998838
Min length2

Characters and Unicode

Total characters201404
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShauna Bannon
2nd rowNicolas Dalby
3rd rowOrion Cosce
4th rowMayra Bueno Silva
5th rowDraw
ValueCountFrequency (%)
silva 196
 
0.6%
matt 196
 
0.6%
chris 188
 
0.6%
nc 172
 
0.5%
mike 144
 
0.5%
alex 140
 
0.4%
john 136
 
0.4%
johnson 132
 
0.4%
anthony 132
 
0.4%
michael 130
 
0.4%
Other values (2402) 29720
95.0%
2024-09-25T19:30:39.670144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20236
 
10.0%
e 16744
 
8.3%
15792
 
7.8%
n 14182
 
7.0%
i 13508
 
6.7%
r 12990
 
6.4%
o 12890
 
6.4%
l 8770
 
4.4%
s 7962
 
4.0%
t 6116
 
3.0%
Other values (46) 72214
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 20236
 
10.0%
e 16744
 
8.3%
15792
 
7.8%
n 14182
 
7.0%
i 13508
 
6.7%
r 12990
 
6.4%
o 12890
 
6.4%
l 8770
 
4.4%
s 7962
 
4.0%
t 6116
 
3.0%
Other values (46) 72214
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 20236
 
10.0%
e 16744
 
8.3%
15792
 
7.8%
n 14182
 
7.0%
i 13508
 
6.7%
r 12990
 
6.4%
o 12890
 
6.4%
l 8770
 
4.4%
s 7962
 
4.0%
t 6116
 
3.0%
Other values (46) 72214
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 20236
 
10.0%
e 16744
 
8.3%
15792
 
7.8%
n 14182
 
7.0%
i 13508
 
6.7%
r 12990
 
6.4%
o 12890
 
6.4%
l 8770
 
4.4%
s 7962
 
4.0%
t 6116
 
3.0%
Other values (46) 72214
35.9%
Distinct2493
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
2024-09-25T19:30:39.904978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length22
Mean length13.121854
Min length5

Characters and Unicode

Total characters203310
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique394 ?
Unique (%)2.5%

Sample

1st rowShauna Bannon
2nd rowClaudio Silva
3rd rowOrion Cosce
4th rowMayra Bueno Silva
5th rowYoussef Zalal
ValueCountFrequency (%)
chris 200
 
0.6%
silva 195
 
0.6%
matt 183
 
0.6%
mike 164
 
0.5%
john 148
 
0.5%
alex 143
 
0.5%
anthony 131
 
0.4%
josh 126
 
0.4%
joe 120
 
0.4%
santos 118
 
0.4%
Other values (3214) 30061
95.2%
2024-09-25T19:30:40.291055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20264
 
10.0%
e 16961
 
8.3%
16095
 
7.9%
n 14312
 
7.0%
i 13756
 
6.8%
o 13224
 
6.5%
r 13003
 
6.4%
l 8893
 
4.4%
s 8074
 
4.0%
t 6236
 
3.1%
Other values (46) 72492
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 20264
 
10.0%
e 16961
 
8.3%
16095
 
7.9%
n 14312
 
7.0%
i 13756
 
6.8%
o 13224
 
6.5%
r 13003
 
6.4%
l 8893
 
4.4%
s 8074
 
4.0%
t 6236
 
3.1%
Other values (46) 72492
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 20264
 
10.0%
e 16961
 
8.3%
16095
 
7.9%
n 14312
 
7.0%
i 13756
 
6.8%
o 13224
 
6.5%
r 13003
 
6.4%
l 8893
 
4.4%
s 8074
 
4.0%
t 6236
 
3.1%
Other values (46) 72492
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 20264
 
10.0%
e 16961
 
8.3%
16095
 
7.9%
n 14312
 
7.0%
i 13756
 
6.8%
o 13224
 
6.5%
r 13003
 
6.4%
l 8893
 
4.4%
s 8074
 
4.0%
t 6236
 
3.1%
Other values (46) 72492
35.7%

Interactions

2024-09-25T19:30:29.323026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:49.138140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:50.803121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:52.443889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:54.066109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:55.699648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:57.254811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:58.951542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:00.496073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:02.211738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:03.977074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:05.792413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:07.353482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:09.150668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:10.760317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:12.416481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:14.010274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:15.649682image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:17.209625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:18.850457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:20.464376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:22.255278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:23.917913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:25.794901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:27.487901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:29.389567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:49.204262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:50.865333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:52.514982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:54.129171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:55.760188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:57.324249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:59.015195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:00.557936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:02.280629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:04.052246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:05.855763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:07.420509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:09.214011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:10.828057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:12.481580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:14.071354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:15.714150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:17.270047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:18.913426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:20.528681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:22.321009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:24.085178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:25.858194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:27.562327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:29.457928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:49.268155image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:50.928351image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:52.584972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:54.190555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:55.824224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:57.389850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:59.078285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:00.623995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:02.352438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-09-25T19:30:05.916179image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:07.488001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:09.279003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:10.890924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:12.546088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:14.220961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:15.778032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:17.423097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:18.975394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:20.597583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:22.383925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:24.149093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:25.922703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:27.742138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:29.525441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:49.330432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:50.989063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:52.652846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:54.253155image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:55.885027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:57.456264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:59.141004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:00.688004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:02.423151image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:04.202097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:05.981098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:07.553632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:09.367248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:10.953906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:12.612427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:14.280836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:15.839046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:17.483292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:19.046893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:20.754098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:22.448274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:24.214372image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:25.994701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:27.809457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:29.599899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:49.453902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:51.052051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:52.722814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:54.315100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:55.946028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:57.520363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:59.204627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:00.754269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:02.493158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:04.273107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:06.043059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:07.619890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:09.431507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:11.016172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:12.676093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:14.343086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:15.904646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:17.544252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:19.113702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:20.818948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:22.508061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:24.281805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:26.063771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:27.875596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:29.663293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:49.513659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-09-25T19:29:52.785780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:54.373028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:56.000369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:57.581096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:59.262152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:00.819017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:02.559936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-09-25T19:30:05.520807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:07.102075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:08.877668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:10.518356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:12.176096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:13.756071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:15.400003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:16.964166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:18.610705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:20.223361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:21.960417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:23.668276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:25.555733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:27.214871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:29.020122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:31.013872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:50.619772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:52.250376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:53.887057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:55.513387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:57.008741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:58.764756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:00.309546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:02.010385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:03.767954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:05.586366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:07.162685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:08.943244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:10.575978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:12.234088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:13.816932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:15.460388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:17.025044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:18.668482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:20.281518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:22.030452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:23.729069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:25.613806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:27.285090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:29.083203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:31.076559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:50.679116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:52.314117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:53.944504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:55.575444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:57.066182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:58.825583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:00.369823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:02.080958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:03.833358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:05.656896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:07.228742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:09.012843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:10.639376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:12.294388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:13.881077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:15.522330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:17.084959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:18.727917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:20.340685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:22.098394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:23.791878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:25.672074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:27.353809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:29.156452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:31.143986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:50.743713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:52.381418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:54.008496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:55.640322image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:57.134912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:29:58.893172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:00.435409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:02.148817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:03.907993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:05.724898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:07.291756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:09.084970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:10.702327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:12.358223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:13.952046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:15.589781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:17.150103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:18.792699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:20.406391image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:22.186996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:23.856701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:25.737785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:27.424736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-25T19:30:29.258149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-09-25T19:30:40.386903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
body_abs_1body_att_1body_def_1body_land_1clinch_abs_1clinch_att_1clinch_def_1clinch_land_1decision_methodfight_duration_lastrndground_abs_1ground_att_1ground_def_1ground_land_1head_abs_1head_att_1head_def_1head_land_1kd_1leg_abs_1leg_att_1leg_def_1leg_land_1td_abs_1td_att_1td_def_1td_land_1
body_abs_11.0000.6110.6840.5840.5910.4510.4430.4250.1220.2210.099-0.0480.077-0.0570.6530.6180.6690.527-0.1140.5530.4660.3470.4620.1460.2930.2540.122
body_att_10.6111.0000.5840.9640.4200.5610.3940.5510.1360.255-0.0540.060-0.0310.0550.5630.7270.6820.650-0.0390.5010.5970.3530.5930.1240.2520.3300.126
body_def_10.6840.5841.0000.5310.3440.3400.3840.3100.1030.186-0.054-0.031-0.017-0.0430.5070.6200.6700.518-0.0430.5440.4800.4410.4720.0550.2150.2330.095
body_land_10.5840.9640.5311.0000.4250.5860.3890.5910.1220.221-0.0570.100-0.0400.0990.5270.6920.6320.653-0.0270.4620.5480.3200.5530.1220.2510.3360.146
clinch_abs_10.5910.4200.3440.4251.0000.6290.6950.6010.0740.1380.1580.0160.1310.0080.5020.3710.4180.335-0.1290.3350.2570.1760.2580.2040.3450.2900.191
clinch_att_10.4510.5610.3400.5860.6291.0000.5880.9630.0840.1510.0190.1500.0310.1410.3710.5060.4170.515-0.0090.2800.3410.1840.3470.2030.3140.3710.203
clinch_def_10.4430.3940.3840.3890.6950.5881.0000.5390.0670.1070.0700.0390.0910.0260.4260.3940.4640.350-0.0760.2830.2510.2060.2460.1530.2840.2770.170
clinch_land_10.4250.5510.3100.5910.6010.9630.5391.0000.0740.1380.0080.1630.0120.1580.3350.4650.3730.502-0.0020.2580.3240.1600.3350.1910.2990.3670.204
decision_method0.1220.1360.1030.1220.0740.0840.0670.0741.0000.4200.0340.0310.0210.0340.1250.1720.1750.1250.1460.1070.1130.0800.1070.0810.1210.1070.081
fight_duration_lastrnd0.2210.2550.1860.2210.1380.1510.1070.1380.4201.0000.0690.0720.0560.0690.2640.3400.3330.2640.0880.2080.2170.1470.2080.1410.1890.1620.141
ground_abs_10.099-0.054-0.054-0.0570.1580.0190.0700.0080.0340.0691.000-0.0270.771-0.0350.350-0.1010.033-0.145-0.221-0.017-0.057-0.074-0.0640.4420.0420.102-0.011
ground_att_1-0.0480.060-0.0310.1000.0160.1500.0390.1630.0310.072-0.0271.0000.0030.974-0.1370.169-0.0490.3480.243-0.055-0.025-0.015-0.013-0.0020.2820.0850.453
ground_def_10.077-0.031-0.017-0.0400.1310.0310.0910.0120.0210.0560.7710.0031.000-0.0110.302-0.0610.096-0.112-0.167-0.008-0.032-0.032-0.0430.3990.0370.091-0.013
ground_land_1-0.0570.055-0.0430.0990.0080.1410.0260.1580.0340.069-0.0350.974-0.0111.000-0.1450.156-0.0640.3500.236-0.064-0.031-0.026-0.017-0.0110.2730.0770.442
head_abs_10.6530.5630.5070.5270.5020.3710.4260.3350.1250.2640.350-0.1370.302-0.1451.0000.6530.7970.526-0.1650.4440.4440.2280.4430.2250.2330.2330.037
head_att_10.6180.7270.6200.6920.3710.5060.3940.4650.1720.340-0.1010.169-0.0610.1560.6531.0000.8270.9120.0170.5480.5420.3800.5440.0830.2700.3300.156
head_def_10.6690.6820.6700.6320.4180.4170.4640.3730.1750.3330.033-0.0490.096-0.0640.7970.8271.0000.683-0.0690.5650.5760.4050.5720.1080.2790.2770.108
head_land_10.5270.6500.5180.6530.3350.5150.3500.5020.1250.264-0.1450.348-0.1120.3500.5260.9120.6831.0000.1030.4430.4260.3010.4440.0370.2730.3110.225
kd_1-0.114-0.039-0.043-0.027-0.129-0.009-0.076-0.0020.1460.088-0.2210.243-0.1670.236-0.1650.017-0.0690.1031.000-0.045-0.036-0.015-0.027-0.143-0.201-0.025-0.148
leg_abs_10.5530.5010.5440.4620.3350.2800.2830.2580.1070.208-0.017-0.055-0.008-0.0640.4440.5480.5650.443-0.0451.0000.4340.5960.4240.0600.1730.2120.062
leg_att_10.4660.5970.4800.5480.2570.3410.2510.3240.1130.217-0.057-0.025-0.032-0.0310.4440.5420.5760.426-0.0360.4341.0000.3450.9770.0700.1830.1990.057
leg_def_10.3470.3530.4410.3200.1760.1840.2060.1600.0800.147-0.074-0.015-0.032-0.0260.2280.3800.4050.301-0.0150.5960.3451.0000.3240.0130.1120.1480.060
leg_land_10.4620.5930.4720.5530.2580.3470.2460.3350.1070.208-0.064-0.013-0.043-0.0170.4430.5440.5720.444-0.0270.4240.9770.3241.0000.0620.1800.2030.060
td_abs_10.1460.1240.0550.1220.2040.2030.1530.1910.0810.1410.442-0.0020.399-0.0110.2250.0830.1080.037-0.1430.0600.0700.0130.0621.000-0.0800.460-0.120
td_att_10.2930.2520.2150.2510.3450.3140.2840.2990.1210.1890.0420.2820.0370.2730.2330.2700.2790.273-0.2010.1730.1830.1120.180-0.0801.000-0.0230.771
td_def_10.2540.3300.2330.3360.2900.3710.2770.3670.1070.1620.1020.0850.0910.0770.2330.3300.2770.311-0.0250.2120.1990.1480.2030.460-0.0231.000-0.030
td_land_10.1220.1260.0950.1460.1910.2030.1700.2040.0810.141-0.0110.453-0.0130.4420.0370.1560.1080.225-0.1480.0620.0570.0600.060-0.1200.771-0.0301.000

Missing values

2024-09-25T19:30:31.266460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-25T19:30:31.575039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fighter_1body_abs_1body_att_1body_def_1body_land_1clinch_abs_1clinch_att_1clinch_def_1clinch_land_1ground_abs_1ground_att_1ground_def_1ground_land_1head_abs_1head_att_1head_def_1head_land_1kd_1leg_abs_1leg_att_1leg_def_1leg_land_1td_abs_1td_att_1td_def_1td_land_1datefight_duration_lastrndfight_duration_lastrnd_timedecision_methodlocationweight_classwinnerfighter
0Shauna Bannon103652235044010421194244051411122202024-07-2735:00Decision - SplitManchester, England, United KingdomWomen's Strawweight BoutShauna BannonShauna Bannon
1Claudio Silva9534161508060321346404303112042022-07-2335:00Decision - UnanimousLondon, England, United KingdomWelterweight BoutNicolas DalbyClaudio Silva
2Orion Cosce261331029148901118283827140915108132022-07-3035:00Decision - UnanimousDallas, Texas, USAWelterweight BoutOrion CosceOrion Cosce
3Mayra Bueno Silva00000000600061510230310002022-08-0611:17SubmissionLas Vegas, Nevada, USAWomen's Bantamweight BoutMayra Bueno SilvaMayra Bueno Silva
4Youssef Zalal6145130705029016101263243011105711412022-08-1335:00Decision - MajoritySan Diego, California, USABantamweight BoutDrawYoussef Zalal
5Daniel Lacerda94132602416124483623141151500002022-08-2013:39KO/TKOSalt Lake City, Utah, USAFlyweight BoutVictor AltamiranoDaniel Lacerda
6Stephanie Egger9333630328177161390032317222022-09-0324:54SubmissionParis, Ile-de-France, FranceWomen's Featherweight BoutStephanie EggerStephanie Egger
7Darian Weeks5192101803000010603614082952009212022-09-1035:00Decision - SplitLas Vegas, Nevada, USAWelterweight BoutYohan LainesseDarian Weeks
8Nikolas Motta436100000100974119221410100002022-09-1713:49KO/TKOLas Vegas, Nevada, USALightweight BoutNikolas MottaNikolas Motta
9Guido Cannetti23230000000005310050401012022-10-0111:04SubmissionLas Vegas, Nevada, USABantamweight BoutGuido CannettiGuido Cannetti
fighter_1body_abs_1body_att_1body_def_1body_land_1clinch_abs_1clinch_att_1clinch_def_1clinch_land_1ground_abs_1ground_att_1ground_def_1ground_land_1head_abs_1head_att_1head_def_1head_land_1kd_1leg_abs_1leg_att_1leg_def_1leg_land_1td_abs_1td_att_1td_def_1td_land_1datefight_duration_lastrndfight_duration_lastrnd_timedecision_methodlocationweight_classwinnerfighter
15484Max Holloway26911662010000004414977621336935700002024-04-1354:59KO/TKOLas Vegas, Nevada, USALightweight BoutMax HollowayMax Holloway
15485Yan Xiaonan1366444036817716846035211919101863332024-04-1355:00Decision - UnanimousLas Vegas, Nevada, USAUFC Women's Strawweight Title BoutZhang WeiliYan Xiaonan
15486Jamahal Hill6603000011030127310693800002024-04-1313:14KO/TKOLas Vegas, Nevada, USAUFC Light Heavyweight Title BoutAlex PereiraJamahal Hill
15487Lukasz Brzeski6172162633801019703127081511540102024-04-0635:00Decision - UnanimousLas Vegas, Nevada, USAHeavyweight BoutLukasz BrzeskiLukasz Brzeski
15488Alex Morono291826902108030141185926017515310502024-04-0635:00Decision - UnanimousLas Vegas, Nevada, USAWelterweight BoutAlex MoronoAlex Morono
15489Charlie Campbell62951810181133221325045200921016210142024-04-0635:00Decision - UnanimousLas Vegas, Nevada, USALightweight BoutCharlie CampbellCharlie Campbell
15490Christos Giagos87137151000026312770670701002024-04-0613:34KO/TKOLas Vegas, Nevada, USALightweight BoutIgnacio BahamondesChristos Giagos
15491Damon Jackson612510016311311120604920061311207032024-04-0635:00Decision - SplitLas Vegas, Nevada, USAFeatherweight BoutDamon JacksonDamon Jackson
15492Chepe Mariscal16191104172120100930945342032411928312024-04-0635:00Decision - SplitLas Vegas, Nevada, USAFeatherweight BoutChepe MariscalChepe Mariscal
15493Chris Curtis253392544420000722051009601531360702024-04-0655:00Decision - SplitLas Vegas, Nevada, USAMiddleweight BoutBrendan AllenChris Curtis